The Strands Agents MCP Server is a model-driven approach to building AI agents in just a few lines of code. It provides curated documentation access to GenAI tools via llms.txt files, enabling AI coding assistants to search and retrieve relevant documentation with intelligent ranking. Features include smart document search, curated content indexing, on-demand fetching, snippet generation, and real URL support. The server can be used with various applications that support MCP servers, such as Amazon Q Developer CLI, Anthropic Claude Code, Cline, and Cursor. Users can quickly test the MCP server using the MCP Inspector and follow the provided steps to configure their MCP client and start using the documentation tools. The project welcomes contributions and is licensed under the Apache License 2.0.

RAG-Powered Odoo Documentation Assistant is a comprehensive documentation processing and chat system that converts Odoo's documentation to a searchable knowledge base with an AI-powered chat interface. It supports multiple Odoo versions (16.0, 17.0, 18.0) and provides semantic search capabilities powered by OpenAI embeddings. The tool automates the conversion of RST to Markdown, offers real-time semantic search, context-aware AI-powered chat responses, and multi-version support. It includes a Streamlit-based web UI, REST API for programmatic access, and a CLI for document processing and chat. The system operates through a pipeline of data processing steps and an interface layer for UI and API access to the knowledge base.

MetaAgent is a multi-agent collaboration platform designed to build, manage, and deploy multi-modal AI agents without the need for coding. Users can easily create AI agents by editing a yml file or using the provided UI. The platform supports features such as building LLM-based AI agents, multi-modal interactions with users using texts, audios, images, and videos, creating a company of agents for complex tasks like drawing comics, vector database and knowledge embeddings, and upcoming features like UI for creating and using AI agents, fine-tuning, and RLHF. The tool simplifies the process of creating and deploying AI agents for various tasks.

The starknet-agent-kit is a toolkit designed for creating AI agents that can interact with the Starknet blockchain. It provides support for multiple AI providers such as Anthropic, OpenAI, Google Gemini, and Ollama. The kit includes an NPM package and a NestJS server with a web interface. Users can run the server in different modes like Chat Mode for conversations, checking balances, executing transfers, and managing accounts, as well as Autonomous Mode for automated monitoring. Additionally, the kit offers a library mode for more advanced usage, allowing users to interact with the StarknetAgent class for executing specific actions. The kit aims to simplify the process of integrating AI capabilities with blockchain interactions.

Avalon-LLM is a repository containing the official code for AvalonBench and the Avalon agent Strategist. AvalonBench evaluates Large Language Models (LLMs) playing The Resistance: Avalon, a board game requiring deductive reasoning, coordination, collaboration, and deception skills. Strategist utilizes LLMs to learn strategic skills through self-improvement, including high-level strategic evaluation and low-level execution guidance. The repository provides instructions for running AvalonBench, setting up Strategist, and conducting experiments with different agents in the game environment.

Run your own AI cluster at home with everyday devices. Exo is experimental software that unifies existing devices into a powerful GPU, supporting wide model compatibility, dynamic model partitioning, automatic device discovery, ChatGPT-compatible API, and device equality. It does not use a master-worker architecture, allowing devices to connect peer-to-peer. Exo supports different partitioning strategies like ring memory weighted partitioning. Installation is recommended from source. Documentation includes example usage on multiple MacOS devices and information on inference engines and networking modules. Known issues include the iOS implementation lagging behind Python.

The `any-llm` repository provides a unified API to access different LLM (Large Language Model) providers. It offers a simple and developer-friendly interface, leveraging official provider SDKs for compatibility and maintenance. The tool is framework-agnostic, actively maintained, and does not require a proxy or gateway server. It addresses challenges in API standardization and aims to provide a consistent interface for various LLM providers, overcoming limitations of existing solutions like LiteLLM, AISuite, and framework-specific integrations.

Perplexity-mcp is a Model Context Protocol (MCP) server that provides web search functionality using Perplexity AI's API. It works with the Anthropic Claude desktop client. The server allows users to search the web with specific queries and filter results by recency. It implements the perplexity_search_web tool, which takes a query as a required argument and can filter results by day, week, month, or year. Users need to set up environment variables, including the PERPLEXITY_API_KEY, to use the server. The tool can be installed via Smithery and requires UV for installation. It offers various models for different contexts and can be added as an MCP server in Cursor or Claude Desktop configurations.

Heygem is an open-source, affordable alternative to Heygen, offering a fully offline video synthesis tool for Windows systems. It enables precise appearance and voice cloning, allowing users to digitalize their image and drive virtual avatars through text and voice for video production. With core features like efficient video synthesis and multi-language support, Heygem ensures a user-friendly experience with fully offline operation and support for multiple models. The tool leverages advanced AI algorithms for voice cloning, automatic speech recognition, and computer vision technology to enhance the virtual avatar's performance and synchronization.

Fraim is an AI-powered toolkit designed for security engineers to enhance their workflows by leveraging AI capabilities. It offers solutions to find, detect, fix, and flag vulnerabilities throughout the development lifecycle. The toolkit includes features like Risk Flagger for identifying risks in code changes, Code Security Analysis for context-aware vulnerability detection, and Infrastructure as Code Analysis for spotting misconfigurations in cloud environments. Fraim can be run as a CLI tool or integrated into Github Actions, making it a versatile solution for security teams and organizations looking to enhance their security practices with AI technology.

Code Assistant is an AI coding tool built in Rust that offers command-line and graphical interfaces for autonomous code analysis and modification. It supports multi-modal tool execution, real-time streaming interface, session-based project management, multiple interface options, and intelligent project exploration. The tool provides auto-loaded repository guidance and allows for project configuration with format-on-save feature. Users can interact with the tool in GUI, terminal, or MCP server mode, and configure LLM providers for advanced options. The architecture highlights adaptive tool syntax, smart tool filtering, and multi-threaded streaming for efficient performance. Contributions are welcome, and the roadmap includes features like block replacing in changed files, compact tool use failures, UI improvements, memory tools, security enhancements, fuzzy matching search blocks, editing user messages, and selecting in messages.

Exosphere is an open source infrastructure designed to run AI agents at scale for large data and long running flows. It allows developers to define plug and playable nodes that can be run on a reliable backbone in the form of a workflow, with features like dynamic state creation at runtime, infinite parallel agents, persistent state management, and failure handling. This enables the deployment of production agents that can scale beautifully to build robust autonomous AI workflows.

Fragments is an open-source tool that leverages Anthropic's Claude Artifacts, Vercel v0, and GPT Engineer. It is powered by E2B Sandbox SDK and Code Interpreter SDK, allowing secure execution of AI-generated code. The tool is based on Next.js 14, shadcn/ui, TailwindCSS, and Vercel AI SDK. Users can stream in the UI, install packages from npm and pip, and add custom stacks and LLM providers. Fragments enables users to build web apps with Python interpreter, Next.js, Vue.js, Streamlit, and Gradio, utilizing providers like OpenAI, Anthropic, Google AI, and more.

Biomni is a general-purpose biomedical AI agent designed to autonomously execute a wide range of research tasks across diverse biomedical subfields. By integrating cutting-edge large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, Biomni helps scientists dramatically enhance research productivity and generate testable hypotheses.

YoMo is an open-source LLM Function Calling Framework for building Geo-distributed AI applications. It is built atop QUIC Transport Protocol and Stateful Serverless architecture, making AI applications low-latency, reliable, secure, and easy. The framework focuses on providing low-latency, secure, stateful serverless functions that can be distributed geographically to bring AI inference closer to end users. It offers features such as low-latency communication, security with TLS v1.3, stateful serverless functions for faster GPU processing, geo-distributed architecture, and a faster-than-real-time codec called Y3. YoMo enables developers to create and deploy stateful serverless functions for AI inference in a distributed manner, ensuring quick responses to user queries from various locations worldwide.

BentoML is an open-source model serving library for building performant and scalable AI applications with Python. It comes with everything you need for serving optimization, model packaging, and production deployment.

The Strands Agents MCP Server is a model-driven approach to building AI agents in just a few lines of code. It provides curated documentation access to GenAI tools via llms.txt files, enabling AI coding assistants to search and retrieve relevant documentation with intelligent ranking. Features include smart document search, curated content indexing, on-demand fetching, snippet generation, and real URL support. The server can be used with various applications that support MCP servers, such as Amazon Q Developer CLI, Anthropic Claude Code, Cline, and Cursor. Users can quickly test the MCP server using the MCP Inspector and follow the provided steps to configure their MCP client and start using the documentation tools. The project welcomes contributions and is licensed under the Apache License 2.0.

The 'yu-picture' project is an educational project that provides complete video tutorials, text tutorials, resume writing, interview question solutions, and Q&A services to help you improve your project skills and enhance your resume. It is an enterprise-level intelligent collaborative cloud image library platform based on Vue 3 + Spring Boot + COS + WebSocket. The platform has a wide range of applications, including public image uploading and retrieval, image analysis for administrators, private image management for individual users, and real-time collaborative image editing for enterprises. The project covers file management, content retrieval, permission control, and real-time collaboration, using various programming concepts, architectural design methods, and optimization strategies to ensure high-speed iteration and stable operation.

TiDB.AI is a conversational search RAG (Retrieval-Augmented Generation) app based on TiDB Serverless Vector Storage. It provides an out-of-the-box and embeddable QA robot experience based on knowledge from official and documentation sites. The platform features a Perplexity-style Conversational Search page with an advanced built-in website crawler for comprehensive coverage. Users can integrate an embeddable JavaScript snippet into their website for instant responses to product-related queries. The tech stack includes Next.js, TypeScript, Tailwind CSS, shadcn/ui for design, TiDB for database storage, Kysely for SQL query building, NextAuth.js for authentication, Vercel for deployments, and LlamaIndex for the RAG framework. TiDB.AI is open-source under the Apache License, Version 2.0.

RAG-Powered Odoo Documentation Assistant is a comprehensive documentation processing and chat system that converts Odoo's documentation to a searchable knowledge base with an AI-powered chat interface. It supports multiple Odoo versions (16.0, 17.0, 18.0) and provides semantic search capabilities powered by OpenAI embeddings. The tool automates the conversion of RST to Markdown, offers real-time semantic search, context-aware AI-powered chat responses, and multi-version support. It includes a Streamlit-based web UI, REST API for programmatic access, and a CLI for document processing and chat. The system operates through a pipeline of data processing steps and an interface layer for UI and API access to the knowledge base.

GitMCP is a free, open-source service that transforms any GitHub project into a remote Model Context Protocol (MCP) endpoint, allowing AI assistants to access project documentation effortlessly. It empowers AI with semantic search capabilities, requires zero setup, is completely free and private, and serves as a bridge between GitHub repositories and AI assistants.

Code Puppy is an AI-powered code generation agent designed to understand programming tasks, generate high-quality code, and explain its reasoning. It supports multi-language code generation, interactive CLI, and detailed code explanations. The tool requires Python 3.9+ and API keys for various models like GPT, Google's Gemini, Cerebras, and Claude. It also integrates with MCP servers for advanced features like code search and documentation lookups. Users can create custom JSON agents for specialized tasks and access a variety of tools for file management, code execution, and reasoning sharing.

UsageRules is a development tool for Elixir projects that helps gather and consolidate usage rules from dependencies to provide to LLM agents. It provides pre-built usage rules for Elixir and a powerful documentation search task for hexdocs. The tool scans project dependencies, looks for `usage-rules.md` files, consolidates rules into a target file, and maintains sections that can be updated independently. It is useful for projects using frameworks like Ash, Phoenix, or other packages that provide specific usage guidelines, coding patterns, or best practices.

The Ollama R library provides an easy way to integrate R with Ollama for running language models locally on your machine. It supports working with standard data structures for different LLMs, offers various output formats, and enables integration with other libraries/tools. The library uses the Ollama REST API and requires the Ollama app to be installed, with GPU support for accelerating LLM inference. It is inspired by Ollama Python and JavaScript libraries, making it familiar for users of those languages. The installation process involves downloading the Ollama app, installing the 'ollamar' package, and starting the local server. Example usage includes testing connection, downloading models, generating responses, and listing available models.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.